Localization Of Multiple Sources Research Articles

Prediction model formulations for detection, enumeration, and localization of multiple sound sources using spherical harmonics

A spherical microphone array is used to detect and localize sound sources in terms of model-based machine learning (ML). In this application, it is crucial to establish parametric models to distinguish background sound environment from presence of sound sources. In the presence of sound sources, the parameter models are also used to localize an unknown number of potentially multiple sound sources. In this work, a model-based Bayesian learning framework is presented for localizing an unknown number of sound sources. Among them, a no-source scenario needs to be accounted for. The model-based machine learning applies the model comparison between the no-source model and the one-source model for sound source detection. After detecting sound sources, the machine learning needs to involve sound source enumeration and localization in order to correctly localize potential multiple sound sources. Specifically, sound environment is analyzed using Bayesian model comparison of two different models accounting for absence and presence of the sound sources for source detection. Upon a positive detection, potentially multiple source models are involved to analyze direction of arrivals (DoAs) for far-field and to localize sound sources for near-field including source distances, amplitudes, and DoAs using Bayesian model selection and parameter estimation.

Multiple Sound Source Localization, Separation, and Reconstruction by Microphone Array: A DNN-Based Approach

Synchronistical localization, separation, and reconstruction for multiple sound sources are usually necessary in various situations, such as in conference rooms, living rooms, and supermarkets. To improve the intelligibility of speech signals, the application of deep neural networks (DNNs) has achieved considerable success in the area of time-domain signal separation and reconstruction. In this paper, we propose a hybrid microphone array signal processing approach for the nearfield scenario that combines the beamforming technique and DNN. Using this method, the challenge of identifying both the sound source location and content can be overcome. Moreover, the use of a sequenced virtual sound field reconstruction process enables the proposed approach to be quite suitable for a sound field which contains a dominant, stronger sound source and masked, weaker sound sources. Using this strategy, all traceable, mainly sound, sources can be discovered by loops in a given sound field. The operational duration and accuracy of localization are further improved by substituting the broadband weighted multiple signal classification (BW-MUSIC) method for the conventional delay-and-sum (DAS) beamforming algorithm. The effectiveness of the proposed method for localizing and reconstructing speech signals was validated by simulations and experiments with promising results. The localization results were accurate, while the similarity and correlation between the reconstructed and original signals was high.

Multiple acoustic source localization using deep data association

Acoustic source localization is a key component of the various acoustic monitoring systems. In this paper, we address the data association problem occurring in the localization of multiple acoustic sources for a distributed acoustic sensor network. The direction of arrival (DOA) of multiple signals is computed at each node and forwarded to a central system for localization. A challenging problem known as data association occurs because DOAs received from different nodes belonging to the same acoustic source are not known. In a realistic environment, the complexity of the data association method increases with the increase in the number of array nodes and sources. With the increase in complexity, the time required for estimating the location of all the sources also increases. Time-critical scenarios of acoustic monitoring require the location to be estimated as close to real-time as possible. To overcome this problem a data-driven deep learning-based solution is presented to control the complexity of the system. A learning approach that formulates the data association among DOAs as combinatorial optimization is proposed. Firstly, features are extracted along with the DOAs of every detected source at each node in the network. Subsequently, a dynamic multiplex Long Short-term memory(LSTM) network is formulated to estimate the association probabilities of the DOAs from different nodes. The methodology is tested using simulations and real data experiments. Comparative analysis is done to validate that the methodology achieves higher association and localization accuracy with a reduced computational time as compared to present state-of-the-art methods.

Sparse Bayesian multiple sources localization using variational approximation for Laplace priors

Multiple sources localization (MSL) has been playing an important role in various applications of wireless sensor networks. In this paper, sparse representation-based MSL with received signal strength (RSS) as observations is addressed within the sparse Bayesian learning framework. Conventional approaches are usually based on the assumption of Gaussian prior for the sparse coefficients. In this paper, Laplacian prior is employed to model the sparse coefficient for improved sparsity performance. To tackle the problem that Laplace prior is not conjugate to Gaussian likelihood, a variational approximation is employed for Bayesian inference. Finally, a multiple sources localization method is developed. Furthermore, to reduce the computing complexity and promote sparsity, a fast multiple sources localization method is further developed. In the proposed methods, the source locations, sparse coefficients, and all necessary model parameters are adaptively deduced solely from the RSS observations. Meanwhile, by employing Laplacian prior for the sparse coefficients, better sparse recovery can be achieved. Experimental results demonstrate the effectiveness of the proposed methods.

Efficient energy-based orthogonal matching pursuit algorithm for multiple sound source localization with unknown source count

In this paper, we propose a compressed sensing (CS) sound source localization algorithm based on signal energy to solve the problem of stopping the iteration condition of the orthogonal matching pursuit (OMP) reconstruction algorithm in CS. The orthogonal matching tracking algorithm needs to stop iteration according to the number of sound sources or the change of residual. Generally, the number of sound sources cannot be known in advance, and the residual often leads to unnecessary calculation. Because the sound source is sparsely distributed in space, and its energy is concentrated and higher than that of the environmental noise, the comparison of the signal energy at different positions in each iteration reconstruction signal is used to determine whether the new sound source is added in this iteration. At the same time, the block sparsity is introduced by using multiple frequency points to avoid the problem of different iteration times for different frequency points in the same frame caused by the uneven energy distribution in the signal frequency domain. Simulation and experimental results show that the proposed algorithm retains the advantages of the orthogonal matching tracking sound source localization algorithm, and can complete the iteration well. Under the premise of not knowing the number of sound sources, the maximum error between the number of iterations and the set number of sound sources is 0.31. The experimental results show that the proposed algorithm has good positioning accuracy and has certain anti-reverberation capability. Compared with other OMP algorithms, the proposed algorithm has better iterative ability and stability. This work is helpful in promoting the development of multiple sound source localization.

A Multiple Sound Source Localization and Counting Method Based on the Kernel Density Estimator

An unambiguous signal processing algorithm when using a wide intermicrophone distance is proposed in this paper for simultaneously locating and counting multiple active sound sources. Based on the kernel density estimator, a multistage structure in the time-frequency domain is used to suppress the influence of spatial aliasing, then the pooled angular spectrum is combined with a peak search method having an updated cut-off threshold and a source merging module. Complete source localization and counting is realized through the combination of these two steps. Simulation results show that the proposed method has a more robust performance than the classic counterpart, especially in adverse environments with spatial aliasing, reverberation, and interference between different sound sources.

3D Multiple Sound Source Localization by Proposed T-Shaped Circular Distributed Microphone Arrays in Combination with GEVD and Adaptive GCC-PHAT/ML Algorithms.

Multiple simultaneous sound source localization (SSL) is one of the most important applications in the speech signal processing. The one-step algorithms with the advantage of low computational complexity (and low accuracy), and the two-step methods with high accuracy (and high computational complexity) are proposed for multiple SSL. In this article, a combination of one-step-based method based on the generalized eigenvalue decomposition (GEVD), and a two-step-based method based on the adaptive generalized cross-correlation (GCC) by using the phase transform/maximum likelihood (PHAT/ML) filters along with a novel T-shaped circular distributed microphone array (TCDMA) is proposed for 3D multiple simultaneous SSL. In addition, the low computational complexity advantage of the GCC algorithm is considered in combination with the high accuracy of the GEVD method by using the distributed microphone array to eliminate spatial aliasing and thus obtain more appropriate information. The proposed T-shaped circular distributed microphone array-based adaptive GEVD and GCC-PHAT/ML algorithms (TCDMA-AGGPM) is compared with hierarchical grid refinement (HiGRID), temporal extension of multiple response model of sparse Bayesian learning with spherical harmonic (SH) extension (SH-TMSBL), sound field morphological component analysis (SF-MCA), and time-frequency mixture weight Bayesian nonparametric acoustical holography beamforming (TF-MW-BNP-AHB) methods based on the mean absolute estimation error (MAEE) criteria in noisy and reverberant environments on simulated and real data. The superiority of the proposed method is presented by showing the high accuracy and low computational complexity for 3D multiple simultaneous SSL.

Real-Time Power Management for Microgrids With Dynamic Boundaries and Multiple Source Locations

The dynamic boundary concept enables more flexible and efficient operation of microgrids with distributed energy resources (DER) that are intermittent in nature. As the integration of renewables continues to accelerate, an adaptive power management module that enables dynamic boundary operations in microgrids with an increasing number of source locations is essential for the fast and low-cost deployment of microgrid controllers. The power management module introduced in this paper is capable of handling the increased complexity in topological variations and transitions stemming from the dynamic boundaries and multiple source locations. This includes real-time operation of multiple islands with dynamic boundaries, initiation of topological transitions (merging and separation of islands), and automatic source coordination for power sharing and frequency regulation. All functions in the power management module are designed to be automatically adaptable to arbitrary microgrids with non-meshed topologies so that the deployment of the controller at new microgrid sites can be expedited with a reduced cost. The module has been implemented on NI’s CompactRIO system as an essential part of an MG controller and tested on a converter-based hardware testbed (HTB). Testing results validated the effectiveness of the algorithms under various operating conditions.

A Hybrid Loop-Tree FEBI Method for Low-Frequency Well Logging of 3-D Structures in Layered Media

Electromagnetic simulation plays an essential role in formation conductivity determination by electromagnetic well-logging tools, especially when the nonvertical borehole and invasion zones are present in a layered earth. A hybrid finite-element boundary integral (FEBI) method is suitable for such well-logging simulations. The usage of layered medium Green's functions allows the simulation background to be a planar stratified medium to characterize the earth formation. However, conventional FEBI using the Rao-Wilton-Glisson (RWG) basis function produces inaccurate results due to the low-frequency breakdown of the boundary integral equation solution. The new contribution of this work is to apply the loop-tree (LT) basis functions in the FEBI method to reduce the numerical error at low frequencies. Such an LT-FEBI method can handle all specific requirements of well-logging simulations at low frequencies. A direct solver is applied to make it more efficient to obtain logging curves with multiple source locations. The LT-FEBI simulation results are validated in several numerical examples, including a challenging well-logging model with a deviated borehole and invasion zones in the Oklahoma formation having 28 layers.

Estimating Locations and Moments of Multiple Dipole-Like Magnetic Sources From Magnetic Gradient Tensor Data Using Differential Evolution

In this article, we present a new method for the detection and localization of multiple dipole-like magnetic sources using magnetic gradient tensor data. We use the tilt angle that is the ratio of the vertical magnetic component to the horizontal field to determine the number of the magnetic sources and use the rotational-invariant normalized source strength (NSS) to estimate approximate horizontal coordinates of the magnetic sources. The differential evolution (DE) algorithm is used to estimate the locations and moments of the magnetic sources. Three synthetic examples, with and without random noise, are designed to validate the proposed method. The locations and magnetic moments of the magnetic sources can be well determined. The proposed method has also been applied to a field test with multiple magnetic sources and the result shows that the location of the magnetic source can be estimated well.

Multiple Sound Source Localization in Three Dimensions Using Convolutional Neural Networks and Clustering Based Post-Processing

Sound source localization methods are successfully applied for various estimation tasks, such as tracking and detecting objects, aiming cameras, and navigating robots. However, large and usually complex distributed microphone arrays are used for three-dimensional acoustic source localization. This study proposes a convolutional neural network architecture for three-dimensional sound source localization using a single tetrahedral microphone array. A spectrum phase component of a microphone array signal was designed as the input of the model, while the output represents a three-dimensional space. The paper provides extensive experimental results of the given method on a semi-synthetic audio data set and a real-world microphone array. Furthermore, cluster-based post-processing has been shown to increase the accuracy of three-dimensional localization by more than 30%. The experimental results on a synthesized data set using the image source method showed 1.08 m localization uncertainties. The estimate of the investigated sound sources had a mean absolute error of 18.97° and elevation error of 48.49°. An additional advantage of the proposed method is the ability to predict the location of the sound source from a single signal analysis frame. This gives instant localization and is in line with many alternative applications. The proposed solution does not require intensive preprocessing of the audio signals and can be used as a video camera pointing system based on a microphone array. In the future, it would be relevant to investigate the localization performance of more than two sound sources, and the variable acoustic conditions could also be assessed.

Deep Learning-Enabled High-Resolution and Fast Sound Source Localization in Spherical Microphone Array System

While sound source localization (SSL) using a spherical microphone array system can be applied to obtain visual beam patterns of source distribution maps in a range of omnidirectional acoustic applications, the present challenges of the spherical measurement system on the valid frequency ranges and the spatial distortion as well as the grid-related limitations of data-driven SSL approaches raise the need to develop an appropriate method. Imbued by these challenges, this study proposes a deep learning approach to achieve the high-resolution performance of localizing multiple sound sources tailored for omnidirectional acoustic applications. First, we present a spherical target map representation that can panoramically pinpoint the position and strength information of multiple sound sources without any grid-related constraints. Then, a dual-branched spherical convolutional autoencoder is proposed to obtain high-resolution localization results from the conventional spherical beamforming maps while incorporating frequency-variant and distortion-invariant strategies to address the inherent challenges. We quantitatively and qualitatively assess our proposed method’s localization capability for multiple sound sources and validate that the proposed method can achieve far more precise and computationally-efficient results than the existing approaches. By extension, we newly present the experimental setup that can create omnidirectional acoustic scenarios for the multiple sound source localization. By evaluating our proposed method in this experimental setup, we demonstrate the effectiveness and applicability of the proposed method with the experimental data. Our study delivers the proposed approach’s potential of being utilized in various sound source localization applications.

Multi-Source DoA Estimation of EM Waves Impinging Spherical Antenna Array With Unknown Mutual Coupling Using Relative Signal Pressure Based Multiple Signal Classification Approach

Spherical antenna array (SAA) is a configuration that scans almost all the radiation sphere with constant directivity. It finds applications in spacecraft and satellite communication. Multiple signal classification (MUSIC) is a widely used multiple source direction-of-arrival (DoA) estimation method because of its low complexity implementation in practical applications. Conversely, it is susceptible to noise, which consequently affects its accuracy of localization. In this paper, MUSIC-based methods that operate at low signal-to-noise ratio (SNR) are developed via relative electromagnetic (EM) wave pressure measurements of a SAA. The proposed methods are the relative pressure MUSIC (RP-MUSIC), and in spherical domain (SH-RP-MUSIC). The developed SH-RP-MUSIC algorithm is in spherical domain thereby allows frequency-smoothing approach for the de-correlation of the coherent source signals towards an enhanced accuracy of localization. Both RP-MUSIC and SH-RP-MUSIC algorithms developed have the ability to estimate the number of active sources that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of the conventional MUSIC algorithm. Numerical experiments were used to demonstrate the adequacy of the developed algorithms. In addition, measured data from experiment, which is the practically acceptable way to examine any procedure is employed to demonstrate the merits of the developed algorithms against the conventional MUSIC algorithm and other recent multiple source localization method in literature. Finally, in order to achieve DoA estimations with adequate localization accuracy at low SNR using SAA, SH-RP-MUSIC algorithm is a better choice.

Localization of Multiple RF Sources via Priori Knowledge-Aided Bayesian Compressive Sensing in UAV-Based WSN

In the unmanned aerial vehicle-based wireless sensor network, the compressive sensing approach can simultaneously locate multiple ground radio frequency sources, while the existing algorithms’ localization accuracies would deteriorate confronting the heavy noise. Hence, we exploit the potential priori knowledge and propose a robust localization algorithm named priori knowledge-aided Bayesian compressive sensing. Firstly, the problem of multi-emitter localization is transformed to that of sparse recovery, whose sparse dictionary is constructed based on the lognormal signal propagation model. Besides, we replace the Gaussian prior in the conventional hierarchical model with the Laplace prior to enhance the localization accuracy. Finally, the sparse recovery problem is solved under the sparse Bayesian learning framework, where the priori knowledge is incorporated to increase localization accuracy further. The improved performance of the proposed algorithm is testified by the simulations where both the state-of-the-art methods and Cramer-Rao lower bound are compared.

Passive localization of multiple sources using joint RSS and AOA measurements in spectrum sharing system

In spectrum sharing systems, locating multiple radiation sources can efficiently find out the intruders, which protects the shared spectrum from malicious jamming or other unauthorized usage. Compared to single-source localization, simultaneously locating multiple sources is more challenging in practice since the association between measurement parameters and source nodes are not known. Moreover, the number of possible measurements-source associations increases exponentially with the number of sensor nodes. It is crucial to discriminate which measurements correspond to the same source before localization. In this work, we propose a centralized localization scheme to estimate the positions of multiple sources. Firstly, we develop two computationally light methods to handle the unknown RSS-AOA measurements-source association problem. One method utilizes linear coordinate conversion to compute the minimum spatial Euclidean distance summation of measurements. Another method exploits the long-short-term memory (LSTM) network to classify the measurement sequences. Then, we propose a weighted least squares (WLS) approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem. Numerical results demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial scenarios where the sources are in close proximity and the measurement noise is strong.

Two-dimensional detection based LRSS point recognition for multi-source DOA estimation

This paper aims to tackle the multiple source localization problem using the signals recorded by a B-format microphone in reverberant environments. The proposed approach analyses the energy and phase information of recorded signals in the time–frequency (T-F) domain to present a two-dimensional detection of low-reverberant-single-source (LRSS) points, where the pressure–velocity energy difference and pressure–velocity phase difference are exploited. The direction of arrival (DOA) estimation of actual sources by utilizing the designed weight kernel density estimation are conducted base on the direction cues derived from the LRSS points. Evaluation on simulated data and data recorded in an actual acoustic chamber demonstrates that a better performance can be obtained through the proposed algorithm in highly reverberant environments.

Multiple source localization using learning-based sparse estimation in deep ocean.

This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source localization in the direct arrival zone of the deep ocean. The GFGRU, trained on sound field replicas of a single source generated by an acoustic propagation model, is used to estimate the ranges and depths of multiple sources without knowing the number of sources. The performance of GFGRU is compared to the Bartlett processor, feedforward neural network (FNN), and sparse Bayesian Learning (SBL) algorithm. Simulations indicate that GFGRU behaves similarly to SBL and offers modest localization performance improvement over the Bartlett and FNN in the presence of array tilt mismatch. The results of real data from the South China Sea also verify the robustness of the proposed GFGRU using a 105 m-aperture vertical array in the deep ocean.

Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks

A method for multiple acoustic source localization using a tetrahedral microphone array and a convolutional neural network (CNN) is presented. Our method presents a novel approach for the estimation of acoustic source direction of arrival (DoA), both azimuth and elevation, utilizing a non-coplanar microphone array. In our approach, we use the phase component of the short-time Fourier transform (STFT) of the microphone array’s signals as the input feature for the CNN and a DoA probability density map as the training target. Our findings imply that our method outperforms the currently available methods for multiple sound source DoA estimation in both accuracy and speed.

Groundwater pollution source identification problems with unknown aquifer parameters by ADGA approach

High-cost remediation of groundwater pollution makes it important to obtain exact information about the source. This is quite difficult to achieve in naturally ill-posed inverse problems of this kind. If the aquifer parameters are also unknown, the problem becomes even more challenging. To address this difficulty, we propose the alternating direction genetic algorithm (ADGA) approach, together with modification of the order of magnitude of the decision variables, to increase the accuracy of the results and computational efficiency. Seven scenarios were designed to test the accuracy of the proposed approach in aquifer with different properties, number of pollution sources, parameters and measurement errors. The results show that combining the ADGA approach with modification of the order of magnitude of the decision variables for identifying both groundwater pollution source and aquifer parameters significantly increases the accuracy of estimated results. The NE value for the estimated results decreased from 9.81% to 58.44% for different cases, and computation time is about half decreased. In addition, the approach is applicable in situations where concentrations of observational data with measurement error, and for multiple source locations and non-uniform media.

Multi-source localization by using offset residual weight

Multiple sound source localization is a hot issue of concern in recent years. The Single Source Zone (SSZ) based localization methods achieve good performance due to the detection and utilization of the Time-Frequency (T-F) zone where only one source is dominant. However, some T-F points consisting of components from multiple sources are also included in the detected SSZ sometimes. Once a T-F point in SSZ is contributed by multiple components, this point is defined as an outlier. The existence of outliers within the detected SSZ is usually an unavoidable problem for SSZ-based methods. To solve this problem, a multi-source localization by using offset residual weight is proposed in this paper. In this method, an assumption is developed: the direction estimated by all the T-F points within the detected SSZ has a difference along with the actual direction of sources. But this difference is much smaller than the difference between the directions estimated by the outliers along with the actual source localization. After verifying this assumption experimentally, Point Offset Residual Weight (PORW) and Source Offset Residual Weight (SORW) are proposed to reduce the influence of outliers on the localization results. Then, a composite weight is formed by combining PORW and SORW, which can effectively distinguish the outliers and desired points. After that, the outliers are removed by composite weight. Finally, a statistical histogram of DOA estimation with outliers removed is used for multi-source localization. The objective evaluation of the proposed method is conducted in various simulated environments. The results show that the proposed method achieves a better performance compared with the reference methods in sources localization.